Optimal transport analysis reveals trajectories in steady-state systems

• Published in: PLoS computational biology Vol. 17; no. 12; p. e1009466
• Main Authors: Zhang, Stephen, Afanassiev, Anton, Greenstreet, Laura, Matsumoto, Tetsuya, Schiebinger, Geoffrey
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.12.2021; Public Library of Science (PLoS)
• Subjects: Arabidopsis - cytology; Arabidopsis thaliana; Biology and Life Sciences; Cell Physiological Phenomena - physiology; Computational Biology - methods; Development; Electronic data processing; Epigenesis, Genetic; Methods; Models, Biological; Physical Sciences; Physiological aspects; Plant Cells - metabolism; Plant Cells - physiology; Plant Roots - cytology; Plants; Research and Analysis Methods; Roots (Botany); Single-Cell Analysis - methods; Time Factors; Canada
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009466

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington’s epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.